A novel integrated automatic strategy for amino acid composition analysis of seeds from 67 species

Yelin Ding; Qirui Bi; Dongdong Huang; Jingmei Liao; Lin Yang; Xiaoxiao Luo; Peilei Yang; Yun Li; Changliang Yao; Wenlong Wei; Jianqing Zhang; Jiayuan Li; Yong Huang; De-An Guo

doi:10.1016/j.foodchem.2023.136670

A novel integrated automatic strategy for amino acid composition analysis of seeds from 67 species

Food Chem. 2023 Nov 15:426:136670. doi: 10.1016/j.foodchem.2023.136670. Epub 2023 Jun 19.

Authors

Affiliations

¹ School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing 210023, China; Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Research Center for TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
² Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Research Center for TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
³ School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing 210023, China; Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Research Center for TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China. Electronic address: daguo@simm.ac.cn.

PMID: 37354578
DOI: 10.1016/j.foodchem.2023.136670

Abstract

The composition and quantity of amino acids (AAs) in seeds are complicated due to the various origins and modifications of different species. In this study, a novel automatic neutral loss filtering (ANLF) strategy based on accurate mass searching by Python was developed to analyze the free and hydrolyzed AA-phenyl isothiocyanate (PITC) derivatives from seeds of Gymnosperm and Angiosperm phyla. Compared with traditional strategies, ANLF showed much higher accuracy in screening AA derivatives by filtering nitrogen-containing non-AA compounds and efficiency in processing large datasets. Meanwhile, the content phenotype of 20 proteinogenic AAs from seeds of these two families was characterized by a 35-min HPLC method combined with an automated peak-matching strategy. AA profiles of 232 batches of seeds from 67 species, consisting of 19 proteinogenic AAs, 21 modified AAs, and 77 unknown AAs, would be a good reference for their application in food and medicine.

Keywords: Amino acids; Automatic neutral loss filtering; Phenyl isothiocyanate; Seeds.

MeSH terms

Amino Acids / analysis
Chromatography, High Pressure Liquid
Cycadopsida* / chemistry
Magnoliopsida* / chemistry
Mass Spectrometry
Phylogeny
Seeds* / chemistry

Substances

Amino Acids